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Diagnostic performance of texture analysis on MRI in grading cerebral gliomas.

Abstract：

BACKGROUND AND PURPOSE:Grading of cerebral gliomas is important both in treatment decision and assessment of prognosis. The purpose of this study was to determine the diagnostic accuracy of grading cerebral gliomas by assessing the tumor heterogeneity using MRI texture analysis (MRTA). MATERIAL AND METHODS:95 patients with gliomas were included, 27 low grade gliomas (LGG) all grade II and 68 high grade gliomas (HGG) (grade III=34 and grade IV=34). Preoperative MRI examinations were performed using a 3T scanner and MRTA was done on preoperative contrast-enhanced three-dimensional isotropic spoiled gradient echo images in a representative ROI. The MRTA was assessed using a commercially available research software program (TexRAD) that applies a filtration-histogram technique for characterizing tumor heterogeneity. Filtration step selectively filters and extracts texture features at different anatomical scales varying from 2mm (fine features) to 6mm (coarse features), the statistical parameter standard deviation (SD) was obtained. Receiver operating characteristics (ROC) was performed to assess sensitivity and specificity for differentiating between the different grades and calculating a threshold value to quantify the heterogeneity. RESULTS:LGG and HGG was best discriminated using SD at fine texture scale, with a sensitivity and specificity of 93% and 81% (AUC 0.910, p<0.0001). The diagnostic ability for MRTA to differentiate between the different sub-groups (grade II-IV) was slightly lower but still significant. CONCLUSIONS:Measuring heterogeneity in gliomas to discriminate HGG from LGG and between different histological sub-types on already obtained images using MRTA can be a useful tool to augment the diagnostic accuracy in grading cerebral gliomas and potentially hasten treatment decision.

journal_name

Eur J Radiol

journal_title

European journal of radiology

authors

Skogen K,Schulz A,Dormagen JB,Ganeshan B,Helseth E,Server A

doi

10.1016/j.ejrad.2016.01.013

subject

Has Abstract

pub_date

2016-04-01 00:00:00

pages

824-9

issue

4

eissn

0720-048X

issn

1872-7727

pii

S0720-048X(16)30013-4

journal_volume

85

pub_type

杂志文章

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